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一种多用户上行放大转发中继系统中快速收敛的信道估计方法

林和昀 袁超伟 杜建和

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一种多用户上行放大转发中继系统中快速收敛的信道估计方法

林和昀, 袁超伟, 杜建和

A fast algorithm with convergence for channel estimation in multi-user uplink amplify-and-forward relay system

Lin He-Yun, Yuan Chao-Wei, Du Jian-He
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  • 针对传统交替最小二乘算法存在的收敛缓慢问题,本文在多用户上行放大转发中继系统中基于Levenberg Marquardt(LM)算法,提出了一种能够快速收敛的信道估计方法,实现了用户-中继信道和中继-基站信道的独立估计.在基站,通过对中继多次放大转发的信号进行建模,构造出具有平行因子结构的三维信号张量模型,并采用LM算法对该模型进行拟合,从而得到系统中两跳链路的信道状态信息.理论分析与仿真结果表明,与已有二线性交替最小二乘方法相比,所提方法具有近乎相同的估计精度;当中继放大因子矩阵为随机矩阵或者包含近似共线性相关列时,所提方法具有更快的收敛速度.
    Recently,tensor models (or multi-way arrays) play a vital role in many applications,such as wireless communication systems,blind source separation,machine learning,signal (audio,image,speech) processing,chemometrics,data mining, arithmetic complexity,environmental sciences,etc.Parallel factor (PARAFAC) analysis,also known as canonical polyadic decomposition,is a common name for low rank decomposition of tensors.A traditional way to fit the PARAFAC model is the alternating least squares (ALS) algorithm,which can transform a nonlinear optimization problem into some independent linear least squares problems.However,the ALS scheme for computing the decomposition of the tensor is known to converge slowly if one or some modes include nearly collinear columns.Particularly,if the collinearity is presented in all modes,the ALS will end in a convergence bottleneck.Hence,it is necessary to develop a robust and fast algorithm to compute the decomposition of the tensor.In this paper,a novel channel estimation algorithm using the Levenberg Marquardt (LM) method based on a third-order tensor model is presented in a multi-user uplink amplify-and-forward (AF) relay system.As the relay nodes all operate with half-duplex mode to aid the transmission,the overall transmission period is partitioned into two transmission subprocesses.In the first transmission sub-process,the users transmit channel training sequence to the relay nodes.This stage requires time block once.During the second transmission sub-process,a set of diagonal amplifying factor matrices are utilized by the relay nodes to amplify the received data.Then,the relay nodes transmit each of the amplified data to the base station.This stage requires time blocks K times.With the help of the channel training sequence and the relay amplifying factor matrices,the received data at the base station can be stacked up into a third-order PARAFAC model. And then based on this tensor model an LM channel estimation algorithm is proposed to provide the individual channel state information of both user-to-relay and relay-to-base station channel links.As the channel sequence is transmitted by the users only once,the proposed scheme has a higher spectral efficiency than the case that the channel sequence is transmitted K times by the users.Numerical experiments are shown to demonstrate the efficacy of the proposed LM channel estimation algorithm.The results are as follows.Firstly,the LM approach has the same channel estimation performance as the bilinear alternating least-squares method.Secondly,the proposed estimator yields much faster convergence speed when the relay amplifying factor matrix is a random matrix or a highly collinear one.Finally,the proposed scheme performs well in both independent identically distributed channels and correlated channels scenarios,which means that the proposed channel estimator can provide the robust and reliable feature for multi-user uplinks AF relay systems.
      通信作者: 袁超伟, yuancw2000@bupt.edu.cn
    • 基金项目: 国家高技术研究发展计划(批准号:2015AA01A705,2014AA01A701)和中国传媒大学理工科规划项目(批准号:3132016XNG1618)资助的课题.
      Corresponding author: Yuan Chao-Wei, yuancw2000@bupt.edu.cn
    • Funds: Project supported by the National High Technology Research and Development Program of China(Grant Nos. 2015AA01A705, 2014AA01A701) and the Science Project of Communication University of China(Grant No. 3132016XNG1618).
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  • [1]

    Sanguinetti L, D'Amico A A, Rong Y 2012 IEEE J. Sel. Areas Commun. 30 1331

    [2]

    Hammerstrom I, Wittneben A 2007 IEEE Trans. Wirel. Commun. 6 2798

    [3]

    Rong Y 2010 IEEE Commun. Lett. 14 390

    [4]

    Munoz M O, Vidal J, Agustin A 2007 IEEE Trans. Signal Process 55 2593

    [5]

    Zhou J, Jiang H, Hisakazu K, Shao G F 2014 Acta Phys. Sin. 63 140506(in Chinese)[周杰, 江浩, 菊池久和, 邵根富2014物理学报63 140506]

    [6]

    Ma L, Liu S Z, Qiao G 2015 Acta Phys. Sin. 64 154304(in Chinese)[马璐, 刘凇佐, 乔钢2015物理学报64 154304]

    [7]

    Sidiropoulos N D, Giannakis G B, Bro R 2000 IEEE Trans. Signal Process 48 810

    [8]

    Kruskal J B 1977 Linear Algebra. Appl. 18 95

    [9]

    Xiao H L, Ouyang S, Nie Z P 2009 Acta Phys. Sin. 58 3685(in Chinese)[肖海林, 欧阳缮, 聂在平2009物理学报58 3685]

    [10]

    de Almeida A L F, Fernandes C A, Da Costa D 2013 IEEE Signal Process. Lett. 20 697

    [11]

    Du J H, Yuan C W, Hu Z W, Lin H Y 2015 IEEE Commun. Lett. 19 1961

    [12]

    Rong Y, Khandaker M R, Xiang Y 2012 IEEE Trans. Wirel. Commun. 11 2224

    [13]

    Du J H, Yuan C W, Zhang J B 2015 IET Commun. 9 737

    [14]

    De Almeida A L F, Favier G, Ximenes L R 2013 IEEE Trans. Signal Process 61 1895

    [15]

    Marquardt D 1963 SIAM J. Appl. Math. 11 431

    [16]

    Nion D, De Lathauwer L 2008 IEEE Trans. Signal Process 56 5567

    [17]

    Tomasi G, Bro R 2006 Comp. Stat. Data Anal. 50 1700

    [18]

    Madsen K, Nielsen H B, Tingleff O 2016 IET Commun. 10 995

    [19]

    Ximenes L R, Favier G, De Almeida A L F, Silva Y C 2014 IEEE Trans. Signal Process 62 3604

    [20]

    Shiu D, Foschini G, Gans M J, Kahn J 2000 IEEE Trans. Commun. 48 502

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出版历程
  • 收稿日期:  2016-06-21
  • 修回日期:  2016-07-27
  • 刊出日期:  2016-11-05

一种多用户上行放大转发中继系统中快速收敛的信道估计方法

  • 1. 北京邮电大学信息与通信工程学院, 北京 100876;
  • 2. 中国传媒大学信息工程学院, 北京 100024
  • 通信作者: 袁超伟, yuancw2000@bupt.edu.cn
    基金项目: 国家高技术研究发展计划(批准号:2015AA01A705,2014AA01A701)和中国传媒大学理工科规划项目(批准号:3132016XNG1618)资助的课题.

摘要: 针对传统交替最小二乘算法存在的收敛缓慢问题,本文在多用户上行放大转发中继系统中基于Levenberg Marquardt(LM)算法,提出了一种能够快速收敛的信道估计方法,实现了用户-中继信道和中继-基站信道的独立估计.在基站,通过对中继多次放大转发的信号进行建模,构造出具有平行因子结构的三维信号张量模型,并采用LM算法对该模型进行拟合,从而得到系统中两跳链路的信道状态信息.理论分析与仿真结果表明,与已有二线性交替最小二乘方法相比,所提方法具有近乎相同的估计精度;当中继放大因子矩阵为随机矩阵或者包含近似共线性相关列时,所提方法具有更快的收敛速度.

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